OpenRaiser/Intern-Atlas huggingface dataset:
Predicting Relative Thresholds for Object Oriented Metrics
Venue: arXiv cs.SE 2021
Year: 2021

Object-oriented software metrics provide a numerical characterization of software quality. They have also been used in the assessment and identification of technical debt. However, metrics generally need to be used with thresholds as reference points that help to interpret their values properly and objectively. The problem is that, while there are many proposed metrics, there are relatively few studies on thresholds and threshold calculation methods; hence, the effective application of metrics in practice has been limited. Moreover, although it has been acknowledged that thresholds should not be absolute, but rather relative to certain contextual factors, the context is still not considered in most threshold studies. In this paper, the relationship between system size (as a contextual factor) and metric thresholds is investigated. The objective is to build predictive models that estimate thresholds based solely on system size, and to assess the feasibility of this approach as a threshold estimation method. An empirical study is conducted for this purpose using 36 defect-prediction datasets and six metrics. The results show that the proposed threshold estimation method is feasible, and it can achieve an accuracy remarkably comparable to more complex threshold models.
RAID: Tool Support for Refactoring-Aware Code Reviews
Venue: arXiv cs.SE 2021
Year: 2021

Code review is a key development practice that contributes to improve software quality and to foster knowledge sharing among developers. However, code review usually takes time and demands detailed and time-consuming analysis of textual diffs. Particularly, detecting refactorings during code reviews is not a trivial task, since they are not explicitly represented in diffs. For example, a Move Function refactoring is represented by deleted (-) and added lines (+) of code which can be located in different and distant source code files. To tackle this problem, we introduce RAID, a refactoring-aware and intelligent diff tool. Besides proposing an architecture for RAID, we implemented a Chrome browser plug-in that supports our solution. Then, we conducted a field experiment with eight professional developers who used RAID for three months. We concluded that RAID can reduce the cognitive effort required for detecting and reviewing refactorings in textual diff. Besides documenting refactorings in diffs, RAID reduces the number of lines required for reviewing such operations. For example, the median number of lines to be reviewed decreases from 14.5 to 2 lines in the case of move refactorings and from 113 to 55 lines in the case of extractions.
Paying Attention to Activation Maps in Camera Pose Regression
Venue: arXiv cs.CV 2021
Year: 2021

Camera pose regression methods apply a single forward pass to the query image to estimate the camera pose. As such, they offer a fast and light-weight alternative to traditional localization schemes based on image retrieval. Pose regression approaches simultaneously learn two regression tasks, aiming to jointly estimate the camera position and orientation using a single embedding vector computed by a convolutional backbone. We propose an attention-based approach for pose regression, where the convolutional activation maps are used as sequential inputs. Transformers are applied to encode the sequential activation maps as latent vectors, used for camera pose regression. This allows us to pay attention to spatially-varying deep features. Using two Transformer heads, we separately focus on the features for camera position and orientation, based on how informative they are per task. Our proposed approach is shown to compare favorably to contemporary pose regressors schemes and achieves state-of-the-art accuracy across multiple outdoor and indoor benchmarks. In particular, to the best of our knowledge, our approach is the only method to attain sub-meter average accuracy across outdoor scenes. We make our code publicly available from here.
Experiences on Managing Technical Debt with Code Smells and AntiPatterns
Venue: arXiv cs.SE 2021
Year: 2021

Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt does not offer actionable advice on how to get rid of it. To get to a practical level in solving problems, more focused mechanisms are needed. Commonly used approaches for this include identifying code smells as quick indications of possible problems in the codebase and detecting the presence of AntiPatterns that refer to overt, recurring problems in design. There are known remedies for both code smells and AntiPatterns. In paper, our goal is to show how to effectively use common tools and the existing body of knowledge on code smells and AntiPatterns to detect technical debt and pay it back. We present two main results: (i) How a combination of static code analysis and manual inspection was used to detect code smells in a codebase leading to the discovery of AntiPatterns; and (ii) How AntiPatterns were used to identify, characterize, and fix problems in the software. The experiences stem from a private company and its long-lasting software product development effort.
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
Venue: arXiv cs.CR 2021
Year: 2021

Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine learning (ML) approach used in these HMDs fail to address the uncertain predictions, including those made on zero-day malware. The ML models used in HMDs are agnostic to the uncertainty that determines whether the model "knows what it knows," severely undermining its trustworthiness. We propose an ensemble-based approach that quantifies uncertainty in predictions made by ML models of an HMD, when it encounters an unknown workload than the ones it was trained on. We test our approach on two different HMDs that have been proposed in the literature. We show that the proposed uncertainty estimator can detect >90% of unknown workloads for the Power-management based HMD, and conclude that the overlapping benign and malware classes undermine the trustworthiness of the Performance Counter-based HMD.
hep_tables: Heterogeneous Array Programming for HEP
Venue: arXiv cs.DB 2021
Year: 2021

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that is the hallmark of the first step of a particle physics analysis: selection, filtering, basic vector operations, and filling histograms. The High Luminosity run of the Large Hadron Collider (HL-LHC), scheduled to start in 2026, will require physicists to regularly skim datasets that are over a PB in size, and repeatedly run over datasets that are 100's of TB's - too big to fit in memory. Declarative programming techniques are a way of separating the intent of the physicist from the mechanics of finding the data, processing the data, and using distributed computing to process it efficiently that is required to extract the plot or data desired in a timely fashion. This paper describes a prototype library that provides a framework for different sub-systems to cooperate in producing this data, using an array-programming declarative interface. This prototype has a ServiceX data-delivery sub-system and an awkward array sub-system cooperating to generate requested data. The ServiceX system runs against ATLAS xAOD data.
A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts
Venue: arXiv cs.CL 2021
Year: 2021

In recent years, Vietnam witnesses the mass development of social network users on different social platforms such as Facebook, Youtube, Instagram, and Tiktok. On social medias, hate speech has become a critical problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process for annotating and evaluating the quality of the dataset. Finally, we evaluated the dataset by deep learning models and transformer models.
ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing
Venue: arXiv cs.CV 2021
Year: 2021

While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications. To tackle these challenges, we propose a novel end-to-end flexible ISTA-unfolding deep network, dubbed ISTA-Net++, with superior performance and strong flexibility. Specifically, by developing a dynamic unfolding strategy, our model enjoys the adaptability of handling CS problems with different ratios, i.e., multi-ratio tasks, through a single model. A cross-block strategy is further utilized to reduce blocking artifacts and enhance the CS recovery quality. Furthermore, we adopt a balanced dataset for training, which brings more robustness when reconstructing images of multiple scenes. Extensive experiments on four datasets show that ISTA-Net++ achieves state-of-the-art results in terms of both quantitative metrics and visual quality. Considering its flexibility, effectiveness and practicability, our model is expected to serve as a suitable baseline in future CS research. The source code is available on https://github.com/jianzhangcs/ISTA-Netpp.
Monolingual and Parallel Corpora for Kangri Low Resource Language
Venue: arXiv cs.CL 2021
Year: 2021

In this paper we present the dataset of Himachali low resource endangered language, Kangri (ISO 639-3xnr) listed in the United Nations Educational, Scientific and Cultural Organization (UNESCO). The compilation of kangri corpus has been a challenging task due to the non-availability of the digitalized resources. The corpus contains 1,81,552 Monolingual and 27,362 Hindi-Kangri Parallel corpora. We shared pre-trained kangri word embeddings. We also reported the Bilingual Evaluation Understudy (BLEU) score and Metric for Evaluation of Translation with Explicit ORdering (METEOR) score of Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) results for the corpus. The corpus is freely available for non-commercial usages and research. To the best of our knowledge, this is the first Himachali low resource endangered language corpus. The resources are available at (https://github.com/chauhanshweta/Kangri_corpus)
PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation
Venue: arXiv cs.CV 2021
Year: 2021

Image animation generates a video of a source image following the motion of a driving video. State-of-the-art self-supervised image animation approaches warp the source image according to the motion of the driving video and recover the warping artifacts by inpainting. These approaches mostly use vanilla convolution for inpainting, and vanilla convolution does not distinguish between valid and invalid pixels. As a result, visual artifacts are still noticeable after inpainting. CutMix is a state-of-the-art regularization strategy that cuts and mixes patches of images and is widely studied in different computer vision tasks. Among the remaining computer vision tasks, warp-based image animation is one of the fields that the effects of CutMix have yet to be studied. This paper first presents a preliminary study on the effects of CutMix on warp-based image animation. We observed in our study that CutMix helps improve only pixel values, but disturbs the spatial relationships between pixels. Based on such observation, we propose PriorityCut, a novel augmentation approach that uses the top-k percent occluded pixels of the foreground to regularize warp-based image animation. By leveraging the domain knowledge in warp-based image animation, PriorityCut significantly reduces the warping artifacts in state-of-the-art warp-based image animation models on diverse datasets.
Complementary Evidence Identification in Open-Domain Question Answering
Venue: arXiv cs.CL 2021
Year: 2021

This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple aspects as to answer a complex question. To this end, we proposes a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. Our experiments demonstrate that our method considers the dependence within the supporting evidence and significantly improves the accuracy of complementary evidence selection in QA domain.
Space Mapping of Spline Spaces over Hierarchical T-meshes
Venue: arXiv cs.CG 2021
Year: 2021

In this paper, we construct a bijective mapping between a biquadratic spline space over the hierarchical T-mesh and the piecewise constant space over the corresponding crossing-vertex-relationship graph (CVR graph). We propose a novel structure, by which we offer an effective and easy operative method for constructing the basis functions of the biquadratic spline space. The mapping we construct is an isomorphism. The basis functions of the biquadratic spline space hold the properties such as linearly independent, completeness and the property of partition of unity, which are the same with the properties for the basis functions of piecewise constant space over the CVR graph. To demonstrate that the new basis functions are efficient, we apply the basis functions to fit some open surfaces.
Exploring Web Search Engines to Find Architectural Knowledge
Venue: arXiv cs.SE 2021
Year: 2021

Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.
TICaM: A Time-of-flight In-car Cabin Monitoring Dataset
Venue: arXiv cs.CV 2021
Year: 2021

We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. Our dataset addresses the deficiencies of currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and provided annotations; all at the same time. We record an exhaustive list of actions performed while driving and provide for them multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation as well as activity annotations for RGB frames. Additional to real recordings, we provide a synthetic dataset of in-car cabin images with same multi-modality of images and annotations, providing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and evaluating domain adaptation approaches. The dataset is available at https://vizta-tof.kl.dfki.de/.
SuSketch: Surrogate Models of Gameplay as a Design Assistant
Venue: arXiv cs.AI 2021
Year: 2021

This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator and to receive varied types of feedback such as path information, predicted balance between players in a complete playthrough, or a predicted heatmap of the locations of player deaths. The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game. SuSketch offers a new way of integrating machine learning into mixed-initiative co-creation tools, as a surrogate of human play trained on a large corpus of artificial playtraces. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.
A New Efficient Numbering System : Application to Numbers Generation and Visual Markers Design
Venue: arXiv cs.CV 2021
Year: 2021

This short paper introduces a recently patented line based numbering system. The last allows a best concordance with decimal digits values, and open up new opportunities, which are not possible with the classical decimal numeration system. Proposed OILU symbolic allows generating a new type of number series, based on multi facets numbers splitting process. On the other hand, this new symbolic is used in the development of new visual markers, highly required in augmented reality and UAV's navigation applications.
Privacy-aware Process Performance Indicators: Framework and Release Mechanisms
Venue: arXiv cs.CR 2021
Year: 2021

Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces control over the data exploited for process monitoring. We then show how PPIs defined based on the established PPINOT meta-model are instantiated in this framework through a set of data release mechanisms. These mechanisms are designed to provide provable guarantees in terms of differential privacy. We evaluate our framework and the release mechanisms in a series of controlled experiments. We further use a public event log to compare our framework with approaches based on privatization of event logs. The results demonstrate feasibility and shed light on the trade-offs between data utility and privacy guarantees in the computation of PPIs.
On how generalised entropies without parameters impact information optimisation processes
Venue: arXiv cs.IT 2021
Year: 2021

As an application of generalised statistical mechanics, it is studied a possible route toward a consistent generalised information theory in terms of a family of non-extensive, non-parametric entropies $H^\pm_D(P)$. Unlike other proposals based on non-extensive entropies with a parameter dependence, our scheme is asymptotically equivalent to the one formulated by Shannon, while it differs in regions where the density of states is reasonably small, which leads to information distributions constrained to their background. Two basic concepts are discussed to this aim. First, we prove two effective coding theorems for the entropies $H^\pm_D(P)$. Then we calculate the channel capacity of a binary symmetric channel (BSC) and a binary erasure channel (BEC) in terms of these entropies. We found that processes such as data compression and channel capacity maximisation can be improved in regions where there is a low density of states, whereas for high densities our results coincide with Shannon's formulation.
Complexity-based permutation entropies: from deterministic time series to white noise
Venue: arXiv cs.IT 2021
Year: 2021

This is a paper in the intersection of time series analysis and complexity theory that presents new results on permutation complexity in general and permutation entropy in particular. In this context, permutation complexity refers to the characterization of time series by means of ordinal patterns (permutations), entropic measures, decay rates of missing ordinal patterns, and more. Since the inception of this \textquotedblleft ordinal\textquotedblright\ methodology, its practical application to any type of scalar time series and real-valued processes have proven to be simple and useful. However, the theoretical aspects have remained limited to noiseless deterministic series and dynamical systems, the main obstacle being the super-exponential growth of visible permutations with length when randomness (also in form of observational noise) is present in the data. To overcome this difficulty, we take a new approach through complexity classes, which are precisely defined by the growth of visible permutations with length, regardless of the deterministic or noisy nature of the data. We consider three major classes: exponential, sub-factorial and factorial. The next step is to adapt the concept of Z-entropy to each of those classes, which we call permutation entropy because it coincides with the conventional permutation entropy on the exponential class. Z-entropies are a family of group entropies, each of them extensive on a given complexity class. The result is a unified approach to the ordinal analysis of deterministic and random processes, from dynamical systems to white noise, with new concepts and tools. Numerical simulations show that permutation entropy discriminates time series from all complexity classes.
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy
Venue: arXiv cs.CL 2021
Year: 2021

Speech Affect Recognition is a problem of extracting emotional affects from audio data. Low resource languages corpora are rear and affect recognition is a difficult task in cross-corpus settings. We present an approach in which the model is trained on high resource language and fine-tune to recognize affects in low resource language. We train the model in same corpus setting on SAVEE, EMOVO, Urdu, and IEMOCAP by achieving baseline accuracy of 60.45, 68.05, 80.34, and 56.58 percent respectively. For capturing the diversity of affects in languages cross-corpus evaluations are discussed in detail. We find that accuracy improves by adding the domain target data into the training data. Finally, we show that performance is improved for low resource language speech affect recognition by achieving the UAR OF 69.32 and 68.2 for Urdu and Italian speech affects.